For developing \(\text{TC-HGR}\)  architecture, we used the raw time-domain sEMG signals. Following the recent literature [1]}, [2]}, [3]}, [4]}, the sEMG signals are pre-processed and smoothed using a 1st order low-pass Butterworth filter. Moreover, to amplify the magnitude of sensors with small values, we scaled the sEMG signals logarithmically [5]}. This technique is known as \(\mu \) -law transformation and is used for quantization in the speech domain. However, we use it for normalizing the sEMG signals, i.e.,
\(F(x_t) = \text{sign}(x_t)\frac{\ln {\big (1+ \mu |x_t|\big )}}{\ln {\big (1+ \mu \big )}},\) 
